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pro vyhledávání: '"Ananthram A"'
Joint Task Offloading and Routing in Wireless Multi-hop Networks Using Biased Backpressure Algorithm
Autor:
Zhao, Zhongyuan, Perazzone, Jake, Verma, Gunjan, Chan, Kevin, Swami, Ananthram, Segarra, Santiago
A significant challenge for computation offloading in wireless multi-hop networks is the complex interaction among traffic flows in the presence of interference. Existing approaches often ignore these key effects and/or rely on outdated queueing and
Externí odkaz:
http://arxiv.org/abs/2412.15385
Autor:
Olshevskyi, Rostyslav, Zhao, Zhongyuan, Chan, Kevin, Verma, Gunjan, Swami, Ananthram, Segarra, Santiago
Graph neural networks (GNNs) are powerful tools for developing scalable, decentralized artificial intelligence in large-scale networked systems, such as wireless networks, power grids, and transportation networks. Currently, GNNs in networked systems
Externí odkaz:
http://arxiv.org/abs/2412.06105
Live comments, also known as Danmaku, are user-generated messages that are synchronized with video content. These comments overlay directly onto streaming videos, capturing viewer emotions and reactions in real-time. While prior work has leveraged li
Externí odkaz:
http://arxiv.org/abs/2410.16407
Autor:
Erfaniantaghvayi, Negar, Zhao, Zhongyuan, Chan, Kevin, Verma, Gunjan, Swami, Ananthram, Segarra, Santiago
A mixture of streaming and short-lived traffic presents a common yet challenging scenario for Backpressure routing in wireless multi-hop networks. Although state-of-the-art shortest-path biased backpressure (SP-BP) can significantly improve the laten
Externí odkaz:
http://arxiv.org/abs/2408.12702
Autor:
Sayyed, Sazzad, Zhang, Milin, Rifat, Shahriar, Swami, Ananthram, De Lucia, Michael, Restuccia, Francesco
In order to deploy deep neural networks (DNNs) in high-stakes scenarios, it is imperative that DNNs provide inference robust to external perturbations - both intentional and unintentional. Although the resilience of DNNs to intentional and unintentio
Externí odkaz:
http://arxiv.org/abs/2408.00193
To reduce the latency of Backpressure (BP) routing in wireless multi-hop networks, we propose to enhance the existing shortest path-biased BP (SP-BP) and sojourn time-based backlog metrics, since they introduce no additional time step-wise signaling
Externí odkaz:
http://arxiv.org/abs/2407.09753
Vision-language models (VLMs) can respond to queries about images in many languages. However, beyond language, culture affects how we see things. For example, individuals from Western cultures focus more on the central figure in an image while indivi
Externí odkaz:
http://arxiv.org/abs/2406.11665
Autor:
Morrill, Todd, Deng, Zhaoyuan, Chen, Yanda, Ananthram, Amith, Leach, Colin Wayne, McKeown, Kathleen
There are many settings where it is useful to predict and explain the success or failure of a dialogue. Circumplex theory from psychology models the social orientations (e.g., Warm-Agreeable, Arrogant-Calculating) of conversation participants and can
Externí odkaz:
http://arxiv.org/abs/2403.04770
Autor:
Chang, Xiangyu, Ahmed, Sk Miraj, Krishnamurthy, Srikanth V., Guler, Basak, Swami, Ananthram, Oymak, Samet, Roy-Chowdhury, Amit K.
The key premise of federated learning (FL) is to train ML models across a diverse set of data-owners (clients), without exchanging local data. An overarching challenge to this date is client heterogeneity, which may arise not only from variations in
Externí odkaz:
http://arxiv.org/abs/2402.08769
We propose a learning-based framework for efficient power allocation in ad hoc interference networks under episodic constraints. The problem of optimal power allocation -- for maximizing a given network utility metric -- under instantaneous constrain
Externí odkaz:
http://arxiv.org/abs/2401.10297